Evidence map›Paper›PMID 41963433›Full record

ArticleScientific reports2026

Estimating the generation time for SARS-CoV-2 transmission using United States household data, December 2021-May 2023.

Louis Yat Hin Chan, Sinead E Morris, Melissa S Stockwell, Natalie M Bowman, Edwin Asturias, Suchitra Rao, Karen Lutrick, Katherine D Ellingson, Huong Q Nguyen, Yvonne Maldonado and 11 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Louis Yat Hin ChanCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA. LouisChan@cdc.gov.
Sinead E MorrisCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
Melissa S StockwellColumbia University Irving Medical Center, New York, NY, USA.
Natalie M BowmanUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Edwin AsturiasUniversity of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA.
Suchitra RaoUniversity of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA.
Karen LutrickUniversity of Arizona, Tucson, AZ, USA.
Katherine D EllingsonUniversity of Arizona, Tucson, AZ, USA.
Huong Q NguyenMarshfield Clinic Research Institute, Marshfield, WI, USA.
Yvonne MaldonadoStanford University, Stanford, CA, USA.
Son H McLarenColumbia University Irving Medical Center, New York, NY, USA.
Ellen SanoColumbia University Irving Medical Center, New York, NY, USA.
Jessica E BiddleCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
Sarah E Smith-JeffcoatCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
Matthew BiggerstaffCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
Melissa A RolfesCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
H Keipp TalbotVanderbilt University Medical Center, Nashville, TN, USA.
Carlos G GrijalvaVanderbilt University Medical Center, Nashville, TN, USA.
Rebecca K BorcheringCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
Alexandra M MellisCenters for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA.
RVTN-Sentinel Study Group

Funding

CDC HHS 75D30121C11656NIH HHS 5UL1TR002243-03
6 · The paper itself

Abstract

Generation time, representing the interval between infection events in primary and secondary cases, is important for understanding disease transmission dynamics including predicting the effective reproduction number (Rt), which informs public health decisions. While previous estimates of SARS-CoV-2 generation times have been reported for early Omicron variants, there is a lack of data for subsequent sub-variants, such as XBB. We estimated SARS-CoV-2 generation times using data from the Respiratory Virus Transmission Network – Sentinel (RVTN-S) household transmission study conducted across seven U.S. sites from December 2021 to May 2023. The study spanned three Omicron sub-periods dominated by the sub-variants BA.1/2, BA.4/5, and XBB. We employed a Susceptible-Exposed-Infectious-Recovered (SEIR) model with a Bayesian data augmentation method that imputes unobserved infection times of cases to estimate the generation time. The estimated mean generation time for the overall Omicron period was 3.5 days (95% credible interval, CrI: 3.3–3.7). During the sub-periods, the estimated mean generation times were 3.8 days (95% CrI: 3.4–4.2) for BA.1/2, 3.5 days (95% CrI: 3.3–3.8) for BA.4/5, and 3.5 days (95% CrI: 3.1–3.9) for XBB. Our study provides estimates of generation times for the Omicron variant, including the sub-variants BA.1/2, BA.4/5, and XBB. These up-to-date estimates specifically address the gap in knowledge regarding these sub-variants and are consistent with earlier studies. They enhance our understanding of SARS-CoV-2 transmission dynamics by aiding in the prediction of Rt, offering insights for improving COVID-19 modeling and public health strategies.

Indexed as

COVID-19SARS-CoV-2Bayes TheoremFamily CharacteristicsHumansTime FactorsUnited States

Identifiers

PMID41963433
PMCPMC13230514

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.